Progeny Selection Engine Using Prediction Scores
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity of selecting the best progenies from a large number of potential options in plant breeding programs makes it difficult to accurately determine the optimal set for advancement, especially when considering trait distribution and genetic diversity.
Innovation Solution
A system and method that utilize a prediction score based on historical phenotypic data and selection algorithms to identify and select a set of progenies for advancement in a breeding pipeline, balancing expected performance and genetic diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional testing methods are used to evaluate all potential progeny, then selection accuracy is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent applies preliminary action by using prediction models to assess progeny potential before actual testing. The system evaluates parent characteristics and genetic markers in advance to predict progeny performance, allowing breeders to prioritize testing only of promising candidates rather than all potential progeny, thus reducing time consumption while maintaining selection accuracy.
Solution Approach 2:
The patent introduces prediction models and genetic markers as intermediary elements between parent selection and progeny testing. These intermediaries provide estimated progeny performance data that guides the selection process, enabling accurate selection without requiring comprehensive testing of all progeny, thereby reducing the time and resources needed.
2Reliability
If comprehensive testing of all progeny is conducted, then selection reliability is improved, but the complexity of the breeding program increases
Solution Approach 1:
The patent segments the breeding program into distinct phases: parent selection, prediction modeling, and targeted progeny testing. By dividing the complex process into manageable segments, the system maintains high selection reliability through structured evaluation while reducing overall program complexity through systematic organization and prioritization of testing activities.
Solution Approach 2:
The patent changes the parameters of evaluation by using prediction models that incorporate genetic markers and parent characteristics instead of requiring direct phenotypic measurement of all progeny. This parameter transformation allows the system to maintain reliable selection decisions while simplifying the breeding program through computational assessment rather than comprehensive physical testing.
3Adaptability or versatility
If traditional phenotypic selection is used, then genetic diversity is maintained, but the ability to predict future performance decreases
Solution Approach 1:
The patent replaces traditional mechanical phenotypic selection methods with computational prediction models that use genetic markers and statistical analysis. This substitution enables more accurate prediction of future performance by analyzing genetic data patterns rather than relying solely on current phenotypic expressions, thereby improving prediction accuracy while maintaining genetic diversity through objective, data-driven selection criteria.
Data Source
AI summary
Exemplary methods for identifying progenies for use in plant breeding are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of progenies and determining a prediction score for at least a portion of the pool of progenies based on the data included in the data structure. The prediction score indicates a probability of selection of the progeny based on historical data. The method further includes selecting a group of progenies from the pool of progenies based on the prediction score, identifying a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies and at least one factor associated with the set of progenies, the pool of progenies and/or the group of progenies, and directing the set of progenies into a validation phase of a breeding pipeline.